Multiple-input multiple-output (MIMO) technology exploits the spatial components of the wireless channel to provide capacity gain and increased link robustness. After almost a decade of research, MIMO wireless communication has finally been adopted in several standards including IEEE 802.16e-2005 and IEEE 802.11n; products based on draft standards are already shipping. MIMO is often combined with OFDM (orthogonal frequency division multiplexing), a type of digital modulation that makes it easy to equalize broadband channels.
In MIMO communication systems, at the transmitter, data are modulated, encoded, and mapped onto spatial signals, which are transmitted from multiple transmit antennas. A main difference with non-MIMO communication systems is that there are many different spatial formatting modes for example beamforming, precoding, spatial multiplexing, space-time coding, and limited feedback precoding, among others (see A. Paulraj, R. Nabar, and D. Gore, Introduction to Space-Time Wireless Communications. 40 West 20th Street, New York, N.Y., USA: Cambridge University Press, 2003 and the references within). The spatial formatting techniques have different performance (in terms of capacity, goodput, achievable rate, or bit error rate for example) in different channel environments. Consequently, an advantageous component of MIMO wireless systems is adapting the transmitted rate in response to channel conditions in what is known as space-time adaptation, link adaptation, or adaptive space-time modulation.
In MIMO communication systems, space-time link adaptation involves adapting the transmitter in response to channel quality information to maximize a performance measure. As one example, prior work considers the joint adaptation of the modulation and coding rate with the spatial formatting to achieve a target performance measure. For example, as described in (R. Heath and A. Paulraj, “Switching between diversity and multiplexing in MIMO systems,” IEEE Trans. Commun., vol. 53, no. 6, pp. 962-968, 2005) the transmitter may switch between a spatial multiplexing spatial formatting method and a spatial diversity spatial formatting method. As described in (S. Catreux, V. Erceg, D. Gesbert, and Heath, R. W., “Adaptive modulation and MIMO coding for broadband wireless data networks,” IEEE Commun. Mag., vol. 40, no. 6, pp. 108-115, 2002), switching between spatial formatting methods substantially improves performance in MIMO wireless communication systems. The high throughput advantages of spatial multiplexing can be achieved when the spatial channel is sufficiently rich while the robustness advantages of spatial diversity can be achieved when the channel is severely fading.
Channel quality information is used to make adaptive modulation, coding, and spatial formatting decisions at the transmitter. Channel quality information may be obtained using a method known as reciprocity where the transmit channel is inferred from the received channel estimate or may be obtained through a feedback channel. When obtained through the use of a feedback channel, channel quality information is computed from measurements made at the receiver. Many different types of channel quality indicators may be used to help make adaptation decisions including signal strength, signal-to-noise ratio (SNR), single-to-interference-plus-noise ratio (SINR), quantized channel state information, limited feedback channel state information, and channel correlation, for example. The receiver may also compute the preferred modulation, coding, and spatial formatting mode and this may also constitute channel quality information.
In prior work, the channel quality information may be used in conjunction with a spatial formatting table to determine the appropriate spatial format. For example, the method in R. Heath and A. Paulraj, “Switching between diversity and multiplexing in MIMO systems,” IEEE Trans. Commun., vol. 53, no. 6, pp. 962-968, 2005, used one bit of feedback from the receiver to improve error rate performance for fixed data rate transmission by switching between space-time block coding and spatial multiplexing. That approach can be combined with link adaptation in a straightforward fashion. The adaptive method in S. Catreux, V. Erceg, D. Gesbert, and Heath, R. W., “Adaptive modulation and MIMO coding for broadband wireless data networks,” IEEE Commun. Mag., vol. 40, no. 6, pp. 108-115, 2002, was designed to enhance spectral efficiency in MIMO-OFDM communication systems using channel quality information in the form of statistical time/frequency selectivity indicators. Spatial correlation information has also been used to implement link adaptation, as described in A. Forenza, M. R. McKay, A. Pandharipande, R. W. Heath, and I. B. Collings, “Adaptive MIMO transmission for exploiting the capacity of spatially correlated channels,” IEEE Trans. Veh. Technol., vol. 56, no. 2, pp. 619-630, March 2007 where statistical beamforming, spatial multiplexing, and double space-time block coding spatial formatting strategies are considered. When correlation based channel quality information is employed, the spatial formatting may be determined by the correlation, while the modulation and coding rate may be determine by other channel quality information and may vary more quickly.
A key assumption in prior work on space-time adaptation, or mode switching, is the absence of interference. Unfortunately, in most cellular wireless systems, especially at the cell edge, the communication link is interference limited. In what is known as the downlink of a cellular system, this means a subscriber being served in one cell receives a non-negligible amount of co-channel interference from transmitters in other cells. The presence of interference reduces the capacity and increases the bit error rate. This makes providing reasonable quality of service to users at the edge of the cell even more challenging.
Typically interference is modeled as colored noise. To compensate for this, receivers typically include a noise whitening filter, which is applied to the received signal based on an estimate of the interference plus noise covariance. As a result, the channel estimated at the receiver includes the effects of the whitening filter, and thus the effects of interference are present in the estimated channel. In wireless communication systems that do not employ MIMO communication technology, the interference may be treated as additional noise power. The corresponding methods for link adaptation may still work in this situation. Unfortunately, interference severely impacts performance in MIMO communication systems, especially when spatial multiplexing is used (see J. G. Andrews, W. Choi, and R. W. Heath, Jr., “Overcoming Interference in Multi-Antenna Cellular Networks,” IEEE Wireless Communications, vol. 14, no. 6, pp. 95-104, December 2007). The reason is that the spatial formatting method in a given wireless cell is impacted by the choice of the spatial formatting method in the interfering cells. This can lead to a competitive scenario where cells update their spatial formatting methods in response to interference, but they themselves create interference forcing neighboring cells to change their spatial formatting method and so on.
The problem of link adaptation in MIMO systems including the effects of interference has been addressed in some prior work. Reference J. H. Kotecha and J. C. Mundarath, “Non-Collaborative Zero-Forcing Beamforming in the Presence of Co-Channel Interference and Spatially Correlated Channels,” Proc. of Veh. Techn. Conf., September 2007. This paper also deals with interference in MIMO systems. It focuses on multi-user MIMO and does not allow any coordination between base stations. It simply derives a statistical solution
Reference A. Szabo, N. Gengf, A. Klein, I. Viering, and J. A. Nossek, “On the performance of fast feedback and link adaptation for MIMO eigenbeamforming in cellular systems,” Proc. of the ITG Workshop on Smart Antennas, pp. 144-151, 2004, studies the impact of interference on spatial mode adaptation in MIMO cellular systems. It also recognizes that even if statistical precoding is used at the transmitter, the optimum rates will change as a function of the interference covariance. This prior work recognizes that there is mismatch but does not propose a concrete solution to the problem of adapting in the presence of changing spatial interference.
Reference Shiming Liu, Xing Zhang, Wenbo Wang, “Analysis of Modulation and Coding Scheme Selection in MIMO-OFDM Systems,” Proc. of Int. Conf. on Comm. and Electronics, pp. 240-245, Oct. 10-11, 2006, includes detailed system level simulations for a MIMO-OFDM system including the effects of interference and hybrid ARQ. This prior work, though, does not allow cooperation between base stations and does not consider the spatial effects of interference.
What is needed, then, is an improved system and method that for link adaptation that overcomes the above-described shortcomings in the prior art.